目的 探讨联合视觉评估钙化积分系统对提高稳定型胸痛患者传统验前概率模型的准确性。方法 回顾性选择门诊行冠脉CT检查的疑似稳定型冠心病患者。根据Update Diamond-Forrester模型(UDFM),计算患者验前概率。计算患者的Agatston和视觉评估钙化积分,记录患者是否存在阻塞性病变。采用重分类改善指数(NRI)对联合钙化积分系统验前概率模型进行验证。结果 共有396例门诊患者符合入组标准。与UDFM模型相比,UDFM+Agatston,UDFM+视觉评估钙化模型能够提高对阻塞性病变的预测价值 [ AUC:0.876(0.830-0.918) vs. 0.749(0.691-0.805),P＜0.001;AUC:0.877(0.835-0.919) vs. 0.749 (0.691-0.805),P＜0.001 ]。与UDFM模型(NRI=27.8.%)比较,视觉评估钙化联合模型的NRI=25.4%(P＜0.05)。结论 在稳定型胸痛人群中,基于CT获得钙化积分可以进一步优化传统验前概率模型,实现更为精准的预测。
Abstract
Objective To investigate the accuracy of the traditional pretest probability model for stable chest pain patients by combining visual evaluation calcification score system.Methods Patients with suspected stable coronary artery disease (CAD) who underwent coronary CT examination in the outpatient department were retrospectively selected.According to the Update Diamond-Forrester model (UDFM), the patient pretest probability was calculated.Agatston and visual assessment calcification scores were calculated and the presence of obstructive lesions was recorded.Reclassification improvement Index (NRI) was used to verify the pretest probability model of combined calcification score system.Results A total of 396 outpatients met the inclusion criteria.Compared with UDFM model, UDFM+Agatston and UDFM+ visual evaluation of calcification model could improve the predictive value of obstructive lesions [AUC: 0.876 (0.830-0.918) vs. 0.749 (0.691-0.805), P<0.001;AUC:0.877 (0.835-0.919) vs. 0.749 (0.691-0.805), P<0.001].Compared with UDFM model (NRI=27.8.%), the NRI of visual assessment combined model of calcification was 25.4% (P<0.05).Conclusion For patients with stable chest pain, calcification score based on CT can further optimize the traditional pretest probability model to achieve more accurate prediction.
关键词
冠脉CT /
验前概率 /
钙化积分 /
冠心病
Key words
coronary computed tomography /
pretest probability /
calcification score /
coronary artery disease
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Knuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes [J]. Eur Heart J, 2020, 41(3): 407-477.
[2] Timmis A, Roobottom C A. National Institute for Health and Care Excellence updates the stable chest pain guideline with radical changes to the diagnostic paradigm [J]. Heart, 2017, 103(13): 982-986.
[3] Fihn S D, Blankenship J C, Alexander K P, et al. 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American college of cardiology/American heart association task force on practice guidelines, and the American association for thoracic surgery, preventive cardiovascular nurses association, society for cardiovascular angiography and interventions, and society of thoracic surgeons [J]. J Am Coll Cardiol, 2014, 64(18): 192919-192949.
[4] Zhou J, Liu Y, Huang L, et al. Validation and comparison of four models to calculate pretest probability of obstructive coronary artery disease in a Chinese population: a coronary computed tomographic angiography study [J]. J Cardiovasc Comput Tomogr, 2017, 11(4): 317-323.
[5] Genders S, Coles A, Hoffmann U, et al. The external validity of prediction models for the diagnosis of obstructive coronary artery disease in patients with stable chest pain: insights from the Promise trial [J]. JACC Cardiovasc Imaging, 2018, 11(3): 437-446.
[6] Zhang Y, Liu Y, Zhang H, et al. Impact of sex-specific differences in calculating the pretest probability of obstructive coronary artery disease in symptomatic patients: a coronary computed tomographic angiography study [J]. Coron Artery Dis, 2019, 30(2): 124-130.
[7] Hecht H S, Cronin P, Blaha M J, et al. 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the society of cardiovascular computed tomography and society of thoracic radiology [J]. J Thorac Imaging, 2017, 32(5): W54-W66.
[8] Shemesh J, Henschke C I, Shaham D, et al. Ordinal scoring of coronary artery calcifications on low-dose CT scans of the chest is predictive of death from cardiovascular disease [J]. Radiology, 2010, 257(2): 541-548.
[9] Cury R C, Abbara S, Achenbach S, et al. CAD-RADS(TM) Coronary Artery Disease - Reporting and Data System. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American society for cardiovascular imaging (NASCI). endorsed by the american college of cardiology [J]. J Cardiovasc Comput Tomogr, 2016, 10(4): 269-281.
[10] Task Force M, Montalescot G, Sechtem U, et al. 2013 ESC guidelines on the management of stable coronary artery disease: the task force on the management of stable coronary artery disease of the European society of cardiology [J]. Eur Heart J, 2013, 34(38): 2949-3003.
[11] Knuuti J, Ballo H, Juarez- Orozco L E, et al. The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability [J]. Eur Heart J, 2018, 39(35): 3322-3230.
[12] Hecht H S, Shaw L, Chandrashekhar Y S, et al. Should NICE guidelines be universally accepted for the evaluation of stable coronary disease? A debate [J]. Eur Heart J, 2019, 40(18): 1440-1453.
[13] Douglas P S, Hoffmann U, Patel M R, et al. Outcomes of anatomical versus functional testing for coronary artery disease [J]. N Engl J Med, 2015, 372(14): 1291-1300.
[14] Lubbers M, Dedic A, Coenen A, et al. Calcium imaging and selective computed tomography angiography in comparison to functional testing for suspected coronary artery disease: the multicentre, randomized crescent trial [J]. Eur Heart J, 2016, 37(15): 1232-1243.
[15] Newby D, Williams M , Hunter A , et al. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial[J]. Lancet, 2015, 385 (9985): 2383-2391.
[16] Genders T S, Steyerberg E W, Hunink M G, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts [J]. BMJ, 2012, 344, e3485.
[17] Takamura K, Kondo T, Fujimoto S, et al. Incremental predictive value for obstructive coronary artery disease by combination of Duke clinical score and Agatston score [J]. Eur Heart J Cardiovasc Imaging, 2016, 17(5): 550-556.
[18] Liu K, Hsieh C, Zhuang N, et al. Current utilization of cardiac computed tomography in mainland China: a national survey [J]. J Cardiovasc Comput Tomogr, 2016, 10(1): 76-81.
[19] Zhou J, Yang J, Yang X, et al. Impact of clinical guideline recommendations on the application of coronary computed tomographic angiography in patients with suspected stable coronary artery disease [J]. Chin Med J (Engl), 2016, 129(2): 135-141.
[20] Winther S, Schmidt S E, Mayrhofer T, et al. Incorporating coronary calcification into pre-test assessment of the likelihood of coronary artery disease [J]. J Am Coll Cardiol, 2020, 76(21): 2421-2432.
[21] Kapoor K, Cainzos-Achirica M, Nasir K. The evolving role of coronary artery calcium in preventive cardiology 30 years after the Agatston score [J]. Curr Opin Cardiol, 2020, 35(5): 500-507.
PDF(993 KB)
